Abstract

This article investigates the output-feedback control ofa class
of stochastic nonlinear system with time-varying delay and unknown
dead zone. Arobust neural stabilizing algorithm is proposed by using
the circle criterion, the NNs approximation and the MLP (minimum learning
parameter) technique. In the scheme, the nonlinear observeris first
designed to estimate the unmeasurable states and the assumption “linear
growthof the nonlinear function is released. Furthermore, the uncertainty
of the wholesystem (including the perturbation of time-varying delay)
is lumped and compensatedby employing one RBF NNs (radial basis function
neural networks). Though, only two weight-norm related parameters
are required tobe updated online for the merit of the MLP technique.
And the gain-inversion relatedadaptive law is targetly designed to
mitigate the adverse effect of unknown dead zone.Comparing with the
previous work, the proposed algorithm obtains the advantage: a conciseform
and easy to implementation due to its less computational burden. The
theoretical analysis andcomparison example demonstrate the substantial
effectiveness of the proposed scheme.

Update

Abstract

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Acknowledgment

This work was supported by National Postdoctoral Program for Innovative
Talents (Grant No. BX201600103), China Postdoctoral Science Foundation
(Grant No. 2016M601600), National Natural Science Foundation of China
(Grant Nos. 61473183, U1509211), and Fundamental Research Funds for
the Central University (Grant No. 3132016001).